add dcn from mmdetection (#693)

* make pixel indexes 0-based for bounding box in pascal voc dataset

* replacing all instances of torch.distributed.deprecated with torch.distributed

* replacing all instances of torch.distributed.deprecated with torch.distributed

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

* add dcn from mmdetection

.gitignore

@@ -28,3 +28,4 @@ dist/
 # project dirs
 /datasets
 /models
+/output

configs/dcn/README.md

+### Reference 
+1 [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/pdf/1811.11168.pdf)  
+2 third-party: [mmdetection](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn)  
+
+### Performance
+|      case                   | bbox AP | mask AP |
+|----------------------------:|--------:|:-------:|
+| R-50-FPN-dcn (implement)    |  39.8   |  -      |
+| R-50-FPN-dcn (mmdetection)  |  40.0   |  -      |
+| R-50-FPN-mdcn (implement)   |  40.0   |  -      |
+| R-50-FPN-mdcn (mmdetection) |  40.3   |  -      |
+| R-50-FPN-dcn (implement)    |  40.8   |  36.8   |
+| R-50-FPN-dcn (mmdetection)  |  41.1   |  37.2   |
+| R-50-FPN-dcn (implement)    |  40.7   |  36.7   |
+| R-50-FPN-dcn (mmdetection)  |  41.4   |  37.4   |
+
+
+### Note
+see [dcn-v2](https://github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md#deformable-convolution-v2) in `mmdetection` for more details.  
+
+
+### Usage
+add these three lines
+```
+MODEL:
+	RESNETS:
+		# corresponding to C2,C3,C4,C5
+		STAGE_WITH_DCN: (False, True, True, True)
+		WITH_MODULATED_DCN: True
+		DEFORMABLE_GROUPS: 1
+```
\ No newline at end of file

configs/dcn/e2e_faster_rcnn_dconv_R_50_FPN_1x.yaml

+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: False
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

configs/dcn/e2e_faster_rcnn_mdconv_R_50_FPN_1x.yaml

+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: True
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

configs/dcn/e2e_mask_rcnn_dconv_R_50_FPN_1x.yaml

+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: False
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
+

configs/dcn/e2e_mask_rcnn_mdconv_R_50_FPN_1x.yaml

+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: True
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

maskrcnn_benchmark/config/defaults.py

@@ -274,6 +274,10 @@ _C.MODEL.RESNETS.BACKBONE_OUT_CHANNELS = 256 * 4
 _C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
 _C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
 
+_C.MODEL.RESNETS.STAGE_WITH_DCN = (False, False, False, False)
+_C.MODEL.RESNETS.WITH_MODULATED_DCN = False
+_C.MODEL.RESNETS.DEFORMABLE_GROUPS = 1
+
 
 # ---------------------------------------------------------------------------- #
 # RetinaNet Options (Follow the Detectron version)

maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu

+// modify from
+// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/modulated_dcn_cuda.c
+
+// based on
+// author: Charles Shang
+// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+#include <THC/THCDeviceUtils.cuh>
+
+#include <vector>
+#include <iostream>
+#include <cmath>
+
+
+void DeformablePSROIPoolForward(
+    const at::Tensor data, const at::Tensor bbox, const at::Tensor trans,
+    at::Tensor out, at::Tensor top_count, const int batch, const int channels,
+    const int height, const int width, const int num_bbox,
+    const int channels_trans, const int no_trans, const float spatial_scale,
+    const int output_dim, const int group_size, const int pooled_size,
+    const int part_size, const int sample_per_part, const float trans_std);
+
+void DeformablePSROIPoolBackwardAcc(
+    const at::Tensor out_grad, const at::Tensor data, const at::Tensor bbox,
+    const at::Tensor trans, const at::Tensor top_count, at::Tensor in_grad,
+    at::Tensor trans_grad, const int batch, const int channels,
+    const int height, const int width, const int num_bbox,
+    const int channels_trans, const int no_trans, const float spatial_scale,
+    const int output_dim, const int group_size, const int pooled_size,
+    const int part_size, const int sample_per_part, const float trans_std);
+
+void deform_psroi_pooling_cuda_forward(
+    at::Tensor input, at::Tensor bbox, at::Tensor trans, at::Tensor out,
+    at::Tensor top_count, const int no_trans, const float spatial_scale,
+    const int output_dim, const int group_size, const int pooled_size,
+    const int part_size, const int sample_per_part, const float trans_std) 
+{
+  AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
+
+  const int batch = input.size(0);
+  const int channels = input.size(1);
+  const int height = input.size(2);
+  const int width = input.size(3);
+  const int channels_trans = no_trans ? 2 : trans.size(1);
+
+  const int num_bbox = bbox.size(0);
+  if (num_bbox != out.size(0))
+    AT_ERROR("Output shape and bbox number wont match: (%d vs %d).",
+             out.size(0), num_bbox);
+
+  DeformablePSROIPoolForward(
+      input, bbox, trans, out, top_count, batch, channels, height, width,
+      num_bbox, channels_trans, no_trans, spatial_scale, output_dim, group_size,
+      pooled_size, part_size, sample_per_part, trans_std);
+}
+
+void deform_psroi_pooling_cuda_backward(
+    at::Tensor out_grad, at::Tensor input, at::Tensor bbox, at::Tensor trans,
+    at::Tensor top_count, at::Tensor input_grad, at::Tensor trans_grad,
+    const int no_trans, const float spatial_scale, const int output_dim,
+    const int group_size, const int pooled_size, const int part_size,
+    const int sample_per_part, const float trans_std) 
+{
+  AT_CHECK(out_grad.is_contiguous(), "out_grad tensor has to be contiguous");
+  AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
+
+  const int batch = input.size(0);
+  const int channels = input.size(1);
+  const int height = input.size(2);
+  const int width = input.size(3);
+  const int channels_trans = no_trans ? 2 : trans.size(1);
+
+  const int num_bbox = bbox.size(0);
+  if (num_bbox != out_grad.size(0))
+    AT_ERROR("Output shape and bbox number wont match: (%d vs %d).",
+             out_grad.size(0), num_bbox);
+
+  DeformablePSROIPoolBackwardAcc(
+      out_grad, input, bbox, trans, top_count, input_grad, trans_grad, batch,
+      channels, height, width, num_bbox, channels_trans, no_trans,
+      spatial_scale, output_dim, group_size, pooled_size, part_size,
+      sample_per_part, trans_std);
+}

maskrcnn_benchmark/csrc/cuda/vision.h

@@ -58,6 +58,59 @@ at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
 at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh);
 
 
+int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
+                             at::Tensor offset, at::Tensor output,
+                             at::Tensor columns, at::Tensor ones, int kW,
+                             int kH, int dW, int dH, int padW, int padH,
+                             int dilationW, int dilationH, int group,
+                             int deformable_group, int im2col_step);
+
+int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
+                                    at::Tensor gradOutput, at::Tensor gradInput,
+                                    at::Tensor gradOffset, at::Tensor weight,
+                                    at::Tensor columns, int kW, int kH, int dW,
+                                    int dH, int padW, int padH, int dilationW,
+                                    int dilationH, int group,
+                                    int deformable_group, int im2col_step);
+
+int deform_conv_backward_parameters_cuda(
+    at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
+    at::Tensor gradWeight,  // at::Tensor gradBias,
+    at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
+    int padW, int padH, int dilationW, int dilationH, int group,
+    int deformable_group, float scale, int im2col_step);
+
+void modulated_deform_conv_cuda_forward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
+    int kernel_h, int kernel_w, const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w, const int dilation_h,
+    const int dilation_w, const int group, const int deformable_group,
+    const bool with_bias);
+
+void modulated_deform_conv_cuda_backward(
+    at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
+    at::Tensor offset, at::Tensor mask, at::Tensor columns,
+    at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
+    at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
+    int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
+    int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
+    const bool with_bias);
+
+void deform_psroi_pooling_cuda_forward(
+    at::Tensor input, at::Tensor bbox, at::Tensor trans, at::Tensor out,
+    at::Tensor top_count, const int no_trans, const float spatial_scale,
+    const int output_dim, const int group_size, const int pooled_size,
+    const int part_size, const int sample_per_part, const float trans_std);
+
+void deform_psroi_pooling_cuda_backward(
+    at::Tensor out_grad, at::Tensor input, at::Tensor bbox, at::Tensor trans,
+    at::Tensor top_count, at::Tensor input_grad, at::Tensor trans_grad,
+    const int no_trans, const float spatial_scale, const int output_dim,
+    const int group_size, const int pooled_size, const int part_size,
+    const int sample_per_part, const float trans_std);
+
+
 at::Tensor compute_flow_cuda(const at::Tensor& boxes,
                              const int height,
                              const int width);

maskrcnn_benchmark/csrc/deform_conv.h

+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+#include "cpu/vision.h"
+
+#ifdef WITH_CUDA
+#include "cuda/vision.h"
+#endif
+
+
+// Interface for Python
+int deform_conv_forward(
+    at::Tensor input, 
+    at::Tensor weight,
+    at::Tensor offset, 
+    at::Tensor output,
+    at::Tensor columns, 
+    at::Tensor ones, 
+    int kW,
+    int kH, 
+    int dW, 
+    int dH, 
+    int padW, 
+    int padH,
+    int dilationW, 
+    int dilationH, 
+    int group,
+    int deformable_group, 
+    int im2col_step)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_forward_cuda(
+        input, weight, offset, output, columns, ones,
+        kW, kH, dW, dH, padW, padH, dilationW, dilationH,
+        group, deformable_group, im2col_step
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+int deform_conv_backward_input(
+    at::Tensor input, 
+    at::Tensor offset,
+    at::Tensor gradOutput, 
+    at::Tensor gradInput,
+    at::Tensor gradOffset, 
+    at::Tensor weight,
+    at::Tensor columns, 
+    int kW, 
+    int kH, 
+    int dW,
+    int dH, 
+    int padW, 
+    int padH, 
+    int dilationW,
+    int dilationH, 
+    int group,
+    int deformable_group, 
+    int im2col_step)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_backward_input_cuda(
+        input, offset, gradOutput, gradInput, gradOffset, weight, columns,
+        kW, kH, dW, dH, padW, padH, dilationW, dilationH, 
+        group, deformable_group, im2col_step
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+int deform_conv_backward_parameters(
+    at::Tensor input, 
+    at::Tensor offset, 
+    at::Tensor gradOutput,
+    at::Tensor gradWeight,  // at::Tensor gradBias,
+    at::Tensor columns, 
+    at::Tensor ones, 
+    int kW, 
+    int kH, 
+    int dW, 
+    int dH,
+    int padW, 
+    int padH, 
+    int dilationW, 
+    int dilationH, 
+    int group,
+    int deformable_group, 
+    float scale, 
+    int im2col_step)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_conv_backward_parameters_cuda(
+        input, offset, gradOutput, gradWeight, columns, ones,
+        kW, kH, dW, dH, padW, padH, dilationW, dilationH,
+        group, deformable_group, scale, im2col_step
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+void modulated_deform_conv_forward(
+    at::Tensor input, 
+    at::Tensor weight, 
+    at::Tensor bias, 
+    at::Tensor ones,
+    at::Tensor offset, 
+    at::Tensor mask, 
+    at::Tensor output, 
+    at::Tensor columns,
+    int kernel_h, 
+    int kernel_w, 
+    const int stride_h, 
+    const int stride_w,
+    const int pad_h, 
+    const int pad_w, 
+    const int dilation_h,
+    const int dilation_w, 
+    const int group, 
+    const int deformable_group,
+    const bool with_bias)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return modulated_deform_conv_cuda_forward(
+        input, weight, bias, ones, offset, mask, output, columns,
+        kernel_h, kernel_w, stride_h, stride_w, 
+        pad_h, pad_w, dilation_h, dilation_w,
+        group, deformable_group, with_bias
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+void modulated_deform_conv_backward(
+    at::Tensor input, 
+    at::Tensor weight, 
+    at::Tensor bias, 
+    at::Tensor ones,
+    at::Tensor offset, 
+    at::Tensor mask, 
+    at::Tensor columns,
+    at::Tensor grad_input, 
+    at::Tensor grad_weight, 
+    at::Tensor grad_bias,
+    at::Tensor grad_offset, 
+    at::Tensor grad_mask, 
+    at::Tensor grad_output,
+    int kernel_h, 
+    int kernel_w, 
+    int stride_h, 
+    int stride_w, 
+    int pad_h,
+    int pad_w, 
+    int dilation_h, 
+    int dilation_w, 
+    int group, 
+    int deformable_group,
+    const bool with_bias)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return modulated_deform_conv_cuda_backward(
+        input, weight, bias, ones, offset, mask, columns, 
+        grad_input, grad_weight, grad_bias, grad_offset, grad_mask, grad_output,
+        kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w,
+        group, deformable_group, with_bias
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
\ No newline at end of file

maskrcnn_benchmark/csrc/deform_pool.h

+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#pragma once
+#include "cpu/vision.h"
+
+#ifdef WITH_CUDA
+#include "cuda/vision.h"
+#endif
+
+
+// Interface for Python
+void deform_psroi_pooling_forward(
+    at::Tensor input, 
+    at::Tensor bbox, 
+    at::Tensor trans, 
+    at::Tensor out,
+    at::Tensor top_count, 
+    const int no_trans, 
+    const float spatial_scale,
+    const int output_dim, 
+    const int group_size, 
+    const int pooled_size,
+    const int part_size, 
+    const int sample_per_part, 
+    const float trans_std)
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_psroi_pooling_cuda_forward(
+        input, bbox, trans, out, top_count, 
+        no_trans, spatial_scale, output_dim, group_size,
+        pooled_size, part_size, sample_per_part, trans_std
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}
+
+
+void deform_psroi_pooling_backward(
+    at::Tensor out_grad, 
+    at::Tensor input, 
+    at::Tensor bbox, 
+    at::Tensor trans,
+    at::Tensor top_count, 
+    at::Tensor input_grad, 
+    at::Tensor trans_grad,
+    const int no_trans, 
+    const float spatial_scale, 
+    const int output_dim,
+    const int group_size, 
+    const int pooled_size, 
+    const int part_size,
+    const int sample_per_part, 
+    const float trans_std) 
+{
+  if (input.type().is_cuda()) {
+#ifdef WITH_CUDA
+    return deform_psroi_pooling_cuda_backward(
+        out_grad, input, bbox, trans, top_count, input_grad, trans_grad,
+        no_trans, spatial_scale, output_dim, group_size, pooled_size, 
+        part_size, sample_per_part, trans_std
+    );
+#else
+    AT_ERROR("Not compiled with GPU support");
+#endif
+  }
+  AT_ERROR("Not implemented on the CPU");
+}

maskrcnn_benchmark/csrc/vision.cpp

@@ -3,6 +3,8 @@
 #include "ROIAlign.h"
 #include "ROIPool.h"
 #include "SigmoidFocalLoss.h"
+#include "deform_conv.h"
+#include "deform_pool.h"
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("nms", &nms, "non-maximum suppression");
@@ -12,4 +14,12 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("roi_pool_backward", &ROIPool_backward, "ROIPool_backward");
   m.def("sigmoid_focalloss_forward", &SigmoidFocalLoss_forward, "SigmoidFocalLoss_forward");
   m.def("sigmoid_focalloss_backward", &SigmoidFocalLoss_backward, "SigmoidFocalLoss_backward");
+  // dcn-v2
+  m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward");
+  m.def("deform_conv_backward_input", &deform_conv_backward_input, "deform_conv_backward_input");
+  m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, "deform_conv_backward_parameters");
+  m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, "modulated_deform_conv_forward");
+  m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, "modulated_deform_conv_backward");
+  m.def("deform_psroi_pooling_forward", &deform_psroi_pooling_forward, "deform_psroi_pooling_forward");
+  m.def("deform_psroi_pooling_backward", &deform_psroi_pooling_backward, "deform_psroi_pooling_backward");
 }
\ No newline at end of file

maskrcnn_benchmark/layers/__init__.py

@@ -3,6 +3,7 @@ import torch
 
 from .batch_norm import FrozenBatchNorm2d
 from .misc import Conv2d
+from .misc import DFConv2d
 from .misc import ConvTranspose2d
 from .misc import BatchNorm2d
 from .misc import interpolate
@@ -13,9 +14,34 @@ from .roi_pool import ROIPool
 from .roi_pool import roi_pool
 from .smooth_l1_loss import smooth_l1_loss
 from .sigmoid_focal_loss import SigmoidFocalLoss
+from .dcn.deform_conv_func import deform_conv, modulated_deform_conv
+from .dcn.deform_conv_module import DeformConv, ModulatedDeformConv, ModulatedDeformConvPack
+from .dcn.deform_pool_func import deform_roi_pooling
+from .dcn.deform_pool_module import DeformRoIPooling, DeformRoIPoolingPack, ModulatedDeformRoIPoolingPack
 
-__all__ = ["nms", "roi_align", "ROIAlign", "roi_pool", "ROIPool",
-           "smooth_l1_loss", "Conv2d", "ConvTranspose2d", "interpolate",
-           "BatchNorm2d", "FrozenBatchNorm2d", "SigmoidFocalLoss"
-          ]
+
+__all__ = [
+    "nms",
+    "roi_align",
+    "ROIAlign",
+    "roi_pool",
+    "ROIPool",
+    "smooth_l1_loss",
+    "Conv2d",
+    "DFConv2d",
+    "ConvTranspose2d",
+    "interpolate",
+    "BatchNorm2d",
+    "FrozenBatchNorm2d",
+    "SigmoidFocalLoss",
+    'deform_conv',
+    'modulated_deform_conv',
+    'DeformConv',
+    'ModulatedDeformConv',
+    'ModulatedDeformConvPack',
+    'deform_roi_pooling',
+    'DeformRoIPooling',
+    'DeformRoIPoolingPack',
+    'ModulatedDeformRoIPoolingPack',
+]
 

maskrcnn_benchmark/layers/dcn/__init__.py

+# 
+# Copied From [mmdetection](https://github.com/open-mmlab/mmdetection/tree/master/mmdet/ops/dcn)
+# 
\ No newline at end of file

maskrcnn_benchmark/layers/dcn/deform_conv_func.py

+import torch
+from torch.autograd import Function
+from torch.nn.modules.utils import _pair
+
+from maskrcnn_benchmark import _C
+
+
+class DeformConvFunction(Function):
+
+    @staticmethod
+    def forward(
+        ctx, 
+        input, 
+        offset, 
+        weight,
+        stride=1, 
+        padding=0, 
+        dilation=1, 
+        groups=1, 
+        deformable_groups=1, 
+        im2col_step=64
+    ):
+        if input is not None and input.dim() != 4:
+            raise ValueError(
+                "Expected 4D tensor as input, got {}D tensor instead.".format(
+                    input.dim()))
+        ctx.stride = _pair(stride)
+        ctx.padding = _pair(padding)
+        ctx.dilation = _pair(dilation)
+        ctx.groups = groups
+        ctx.deformable_groups = deformable_groups
+        ctx.im2col_step = im2col_step
+
+        ctx.save_for_backward(input, offset, weight)
+
+        output = input.new_empty(
+            DeformConvFunction._output_size(input, weight, ctx.padding,
+                                            ctx.dilation, ctx.stride))
+
+        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
+
+        if not input.is_cuda:
+            raise NotImplementedError
+        else:
+            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
+            assert (input.shape[0] %
+                    cur_im2col_step) == 0, 'im2col step must divide batchsize'
+            _C.deform_conv_forward(
+                input, 
+                weight, 
+                offset, 
+                output, 
+                ctx.bufs_[0], 
+                ctx.bufs_[1],
+                weight.size(3), 
+                weight.size(2), 
+                ctx.stride[1], 
+                ctx.stride[0],
+                ctx.padding[1], 
+                ctx.padding[0], 
+                ctx.dilation[1],
+                ctx.dilation[0], 
+                ctx.groups, 
+                ctx.deformable_groups,
+                cur_im2col_step
+            )
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input, offset, weight = ctx.saved_tensors
+
+        grad_input = grad_offset = grad_weight = None
+
+        if not grad_output.is_cuda:
+            raise NotImplementedError
+        else:
+            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
+            assert (input.shape[0] %
+                    cur_im2col_step) == 0, 'im2col step must divide batchsize'
+
+            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
+                grad_input = torch.zeros_like(input)
+                grad_offset = torch.zeros_like(offset)
+                _C.deform_conv_backward_input(
+                    input, 
+                    offset, 
+                    grad_output, 
+                    grad_input,
+                    grad_offset, 
+                    weight, 
+                    ctx.bufs_[0], 
+                    weight.size(3),
+                    weight.size(2), 
+                    ctx.stride[1], 
+                    ctx.stride[0],
+                    ctx.padding[1], 
+                    ctx.padding[0], 
+                    ctx.dilation[1],
+                    ctx.dilation[0], 
+                    ctx.groups, 
+                    ctx.deformable_groups,
+                    cur_im2col_step
+                )
+
+            if ctx.needs_input_grad[2]:
+                grad_weight = torch.zeros_like(weight)
+                _C.deform_conv_backward_parameters(
+                    input, 
+                    offset, 
+                    grad_output,
+                    grad_weight, 
+                    ctx.bufs_[0], 
+                    ctx.bufs_[1], 
+                    weight.size(3),
+                    weight.size(2), 
+                    ctx.stride[1], 
+                    ctx.stride[0],
+                    ctx.padding[1], 
+                    ctx.padding[0], 
+                    ctx.dilation[1],
+                    ctx.dilation[0], 
+                    ctx.groups, 
+                    ctx.deformable_groups, 
+                    1,
+                    cur_im2col_step
+                )
+
+        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)
+
+    @staticmethod
+    def _output_size(input, weight, padding, dilation, stride):
+        channels = weight.size(0)
+        output_size = (input.size(0), channels)
+        for d in range(input.dim() - 2):
+            in_size = input.size(d + 2)
+            pad = padding[d]
+            kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
+            stride_ = stride[d]
+            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
+        if not all(map(lambda s: s > 0, output_size)):
+            raise ValueError(
+                "convolution input is too small (output would be {})".format(
+                    'x'.join(map(str, output_size))))
+        return output_size
+
+
+class ModulatedDeformConvFunction(Function):
+
+    @staticmethod
+    def forward(
+        ctx,
+        input,
+        offset,
+        mask,
+        weight,
+        bias=None,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        deformable_groups=1
+    ):
+        ctx.stride = stride
+        ctx.padding = padding
+        ctx.dilation = dilation
+        ctx.groups = groups
+        ctx.deformable_groups = deformable_groups
+        ctx.with_bias = bias is not None
+        if not ctx.with_bias:
+            bias = input.new_empty(1)  # fake tensor
+        if not input.is_cuda:
+            raise NotImplementedError
+        if weight.requires_grad or mask.requires_grad or offset.requires_grad \
+                or input.requires_grad:
+            ctx.save_for_backward(input, offset, mask, weight, bias)
+        output = input.new_empty(
+            ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
+        ctx._bufs = [input.new_empty(0), input.new_empty(0)]
+        _C.modulated_deform_conv_forward(
+            input, 
+            weight, 
+            bias, 
+            ctx._bufs[0], 
+            offset, 
+            mask, 
+            output,
+            ctx._bufs[1], 
+            weight.shape[2], 
+            weight.shape[3], 
+            ctx.stride,
+            ctx.stride, 
+            ctx.padding, 
+            ctx.padding, 
+            ctx.dilation, 
+            ctx.dilation,
+            ctx.groups, 
+            ctx.deformable_groups, 
+            ctx.with_bias
+        )
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if not grad_output.is_cuda:
+            raise NotImplementedError
+        input, offset, mask, weight, bias = ctx.saved_tensors
+        grad_input = torch.zeros_like(input)
+        grad_offset = torch.zeros_like(offset)
+        grad_mask = torch.zeros_like(mask)
+        grad_weight = torch.zeros_like(weight)
+        grad_bias = torch.zeros_like(bias)
+        _C.modulated_deform_conv_backward(
+            input, 
+            weight, 
+            bias, 
+            ctx._bufs[0], 
+            offset, 
+            mask, 
+            ctx._bufs[1],
+            grad_input, 
+            grad_weight, 
+            grad_bias, 
+            grad_offset, 
+            grad_mask,
+            grad_output, 
+            weight.shape[2], 
+            weight.shape[3], 
+            ctx.stride,
+            ctx.stride, 
+            ctx.padding, 
+            ctx.padding, 
+            ctx.dilation, 
+            ctx.dilation,
+            ctx.groups, 
+            ctx.deformable_groups, 
+            ctx.with_bias
+        )
+        if not ctx.with_bias:
+            grad_bias = None
+
+        return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
+                None, None, None, None, None)
+
+    @staticmethod
+    def _infer_shape(ctx, input, weight):
+        n = input.size(0)
+        channels_out = weight.size(0)
+        height, width = input.shape[2:4]
+        kernel_h, kernel_w = weight.shape[2:4]
+        height_out = (height + 2 * ctx.padding -
+                      (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1
+        width_out = (width + 2 * ctx.padding -
+                     (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1
+        return n, channels_out, height_out, width_out
+
+
+deform_conv = DeformConvFunction.apply
+modulated_deform_conv = ModulatedDeformConvFunction.apply

maskrcnn_benchmark/layers/dcn/deform_conv_module.py

+import math
+
+import torch
+import torch.nn as nn
+from torch.nn.modules.utils import _pair
+
+from .deform_conv_func import deform_conv, modulated_deform_conv
+
+
+class DeformConv(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        deformable_groups=1,
+        bias=False
+    ):
+        assert not bias
+        super(DeformConv, self).__init__()
+        self.with_bias = bias
+
+        assert in_channels % groups == 0, \
+            'in_channels {} cannot be divisible by groups {}'.format(
+                in_channels, groups)
+        assert out_channels % groups == 0, \
+            'out_channels {} cannot be divisible by groups {}'.format(
+                out_channels, groups)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = _pair(stride)
+        self.padding = _pair(padding)
+        self.dilation = _pair(dilation)
+        self.groups = groups
+        self.deformable_groups = deformable_groups
+
+        self.weight = nn.Parameter(
+            torch.Tensor(out_channels, in_channels // self.groups,
+                         *self.kernel_size))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+
+    def forward(self, input, offset):
+        return deform_conv(input, offset, self.weight, self.stride,
+                           self.padding, self.dilation, self.groups,
+                           self.deformable_groups)
+
+    def __repr__(self):
+        return "".join([
+            "{}(".format(self.__class__.__name__),
+            "in_channels={}, ".format(self.in_channels),
+            "out_channels={}, ".format(self.out_channels),
+            "kernel_size={}, ".format(self.kernel_size),
+            "stride={}, ".format(self.stride),
+            "dilation={}, ".format(self.dilation),
+            "padding={}, ".format(self.padding),
+            "groups={}, ".format(self.groups),
+            "deformable_groups={}, ".format(self.deformable_groups),
+            "bias={})".format(self.with_bias),
+        ])
+
+
+class ModulatedDeformConv(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        deformable_groups=1,
+        bias=True
+    ):
+        super(ModulatedDeformConv, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.groups = groups
+        self.deformable_groups = deformable_groups
+        self.with_bias = bias
+
+        self.weight = nn.Parameter(torch.Tensor(
+            out_channels, 
+            in_channels // groups,
+            *self.kernel_size
+        ))
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.zero_()
+
+    def forward(self, input, offset, mask):
+        return modulated_deform_conv(
+            input, offset, mask, self.weight, self.bias, self.stride,
+            self.padding, self.dilation, self.groups, self.deformable_groups)
+
+    def __repr__(self):
+        return "".join([
+            "{}(".format(self.__class__.__name__),
+            "in_channels={}, ".format(self.in_channels),
+            "out_channels={}, ".format(self.out_channels),
+            "kernel_size={}, ".format(self.kernel_size),
+            "stride={}, ".format(self.stride),
+            "dilation={}, ".format(self.dilation),
+            "padding={}, ".format(self.padding),
+            "groups={}, ".format(self.groups),
+            "deformable_groups={}, ".format(self.deformable_groups),
+            "bias={})".format(self.with_bias),
+        ])
+
+class ModulatedDeformConvPack(ModulatedDeformConv):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 deformable_groups=1,
+                 bias=True):
+        super(ModulatedDeformConvPack, self).__init__(
+            in_channels, out_channels, kernel_size, stride, padding, dilation,
+            groups, deformable_groups, bias)
+
+        self.conv_offset_mask = nn.Conv2d(
+            self.in_channels // self.groups,
+            self.deformable_groups * 3 * self.kernel_size[0] *
+            self.kernel_size[1],
+            kernel_size=self.kernel_size,
+            stride=_pair(self.stride),
+            padding=_pair(self.padding),
+            bias=True)
+        self.init_offset()
+
+    def init_offset(self):
+        self.conv_offset_mask.weight.data.zero_()
+        self.conv_offset_mask.bias.data.zero_()
+
+    def forward(self, input):
+        out = self.conv_offset_mask(input)
+        o1, o2, mask = torch.chunk(out, 3, dim=1)
+        offset = torch.cat((o1, o2), dim=1)
+        mask = torch.sigmoid(mask)
+        return modulated_deform_conv(
+            input, offset, mask, self.weight, self.bias, self.stride,
+            self.padding, self.dilation, self.groups, self.deformable_groups)

maskrcnn_benchmark/layers/dcn/deform_pool_func.py

+import torch
+from torch.autograd import Function
+
+from maskrcnn_benchmark import _C
+
+
+class DeformRoIPoolingFunction(Function):
+
+    @staticmethod
+    def forward(
+        ctx,
+        data,
+        rois,
+        offset,
+        spatial_scale,
+        out_size,
+        out_channels,
+        no_trans,
+        group_size=1,
+        part_size=None,
+        sample_per_part=4,
+        trans_std=.0
+    ):
+        ctx.spatial_scale = spatial_scale
+        ctx.out_size = out_size
+        ctx.out_channels = out_channels
+        ctx.no_trans = no_trans
+        ctx.group_size = group_size
+        ctx.part_size = out_size if part_size is None else part_size
+        ctx.sample_per_part = sample_per_part
+        ctx.trans_std = trans_std
+
+        assert 0.0 <= ctx.trans_std <= 1.0
+        if not data.is_cuda:
+            raise NotImplementedError
+
+        n = rois.shape[0]
+        output = data.new_empty(n, out_channels, out_size, out_size)
+        output_count = data.new_empty(n, out_channels, out_size, out_size)
+        _C.deform_psroi_pooling_forward(
+            data, 
+            rois, 
+            offset, 
+            output, 
+            output_count, 
+            ctx.no_trans,
+            ctx.spatial_scale, 
+            ctx.out_channels, 
+            ctx.group_size, 
+            ctx.out_size,
+            ctx.part_size, 
+            ctx.sample_per_part, 
+            ctx.trans_std
+        )
+
+        if data.requires_grad or rois.requires_grad or offset.requires_grad:
+            ctx.save_for_backward(data, rois, offset)
+        ctx.output_count = output_count
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if not grad_output.is_cuda:
+            raise NotImplementedError
+
+        data, rois, offset = ctx.saved_tensors
+        output_count = ctx.output_count
+        grad_input = torch.zeros_like(data)
+        grad_rois = None
+        grad_offset = torch.zeros_like(offset)
+
+        _C.deform_psroi_pooling_backward(
+            grad_output, 
+            data, 
+            rois, 
+            offset, 
+            output_count, 
+            grad_input,
+            grad_offset, 
+            ctx.no_trans, 
+            ctx.spatial_scale, 
+            ctx.out_channels,
+            ctx.group_size, 
+            ctx.out_size, 
+            ctx.part_size, 
+            ctx.sample_per_part,
+            ctx.trans_std
+        )
+        return (grad_input, grad_rois, grad_offset, None, None, None, None, None, None, None, None)
+
+
+deform_roi_pooling = DeformRoIPoolingFunction.apply

maskrcnn_benchmark/layers/dcn/deform_pool_module.py

+from torch import nn
+
+from .deform_pool_func import deform_roi_pooling
+
+
+class DeformRoIPooling(nn.Module):
+
+    def __init__(self,
+                 spatial_scale,
+                 out_size,
+                 out_channels,
+                 no_trans,
+                 group_size=1,
+                 part_size=None,
+                 sample_per_part=4,
+                 trans_std=.0):
+        super(DeformRoIPooling, self).__init__()
+        self.spatial_scale = spatial_scale
+        self.out_size = out_size
+        self.out_channels = out_channels
+        self.no_trans = no_trans
+        self.group_size = group_size
+        self.part_size = out_size if part_size is None else part_size
+        self.sample_per_part = sample_per_part
+        self.trans_std = trans_std
+
+    def forward(self, data, rois, offset):
+        if self.no_trans:
+            offset = data.new_empty(0)
+        return deform_roi_pooling(
+            data, rois, offset, self.spatial_scale, self.out_size,
+            self.out_channels, self.no_trans, self.group_size, self.part_size,
+            self.sample_per_part, self.trans_std)
+
+
+class DeformRoIPoolingPack(DeformRoIPooling):
+
+    def __init__(self,
+                 spatial_scale,
+                 out_size,
+                 out_channels,
+                 no_trans,
+                 group_size=1,
+                 part_size=None,
+                 sample_per_part=4,
+                 trans_std=.0,
+                 deform_fc_channels=1024):
+        super(DeformRoIPoolingPack,
+              self).__init__(spatial_scale, out_size, out_channels, no_trans,
+                             group_size, part_size, sample_per_part, trans_std)
+
+        self.deform_fc_channels = deform_fc_channels
+
+        if not no_trans:
+            self.offset_fc = nn.Sequential(
+                nn.Linear(self.out_size * self.out_size * self.out_channels,
+                          self.deform_fc_channels),
+                nn.ReLU(inplace=True),
+                nn.Linear(self.deform_fc_channels, self.deform_fc_channels),
+                nn.ReLU(inplace=True),
+                nn.Linear(self.deform_fc_channels,
+                          self.out_size * self.out_size * 2))
+            self.offset_fc[-1].weight.data.zero_()
+            self.offset_fc[-1].bias.data.zero_()
+
+    def forward(self, data, rois):
+        assert data.size(1) == self.out_channels
+        if self.no_trans:
+            offset = data.new_empty(0)
+            return deform_roi_pooling(
+                data, rois, offset, self.spatial_scale, self.out_size,
+                self.out_channels, self.no_trans, self.group_size,
+                self.part_size, self.sample_per_part, self.trans_std)
+        else:
+            n = rois.shape[0]
+            offset = data.new_empty(0)
+            x = deform_roi_pooling(data, rois, offset, self.spatial_scale,
+                                   self.out_size, self.out_channels, True,
+                                   self.group_size, self.part_size,
+                                   self.sample_per_part, self.trans_std)
+            offset = self.offset_fc(x.view(n, -1))
+            offset = offset.view(n, 2, self.out_size, self.out_size)
+            return deform_roi_pooling(
+                data, rois, offset, self.spatial_scale, self.out_size,
+                self.out_channels, self.no_trans, self.group_size,
+                self.part_size, self.sample_per_part, self.trans_std)
+
+
+class ModulatedDeformRoIPoolingPack(DeformRoIPooling):
+
+    def __init__(self,
+                 spatial_scale,
+                 out_size,
+                 out_channels,
+                 no_trans,
+                 group_size=1,
+                 part_size=None,
+                 sample_per_part=4,
+                 trans_std=.0,
+                 deform_fc_channels=1024):
+        super(ModulatedDeformRoIPoolingPack, self).__init__(
+            spatial_scale, out_size, out_channels, no_trans, group_size,
+            part_size, sample_per_part, trans_std)
+
+        self.deform_fc_channels = deform_fc_channels
+
+        if not no_trans:
+            self.offset_fc = nn.Sequential(
+                nn.Linear(self.out_size * self.out_size * self.out_channels,
+                          self.deform_fc_channels),
+                nn.ReLU(inplace=True),
+                nn.Linear(self.deform_fc_channels, self.deform_fc_channels),
+                nn.ReLU(inplace=True),
+                nn.Linear(self.deform_fc_channels,
+                          self.out_size * self.out_size * 2))
+            self.offset_fc[-1].weight.data.zero_()
+            self.offset_fc[-1].bias.data.zero_()
+            self.mask_fc = nn.Sequential(
+                nn.Linear(self.out_size * self.out_size * self.out_channels,
+                          self.deform_fc_channels),
+                nn.ReLU(inplace=True),
+                nn.Linear(self.deform_fc_channels,
+                          self.out_size * self.out_size * 1),
+                nn.Sigmoid())
+            self.mask_fc[2].weight.data.zero_()
+            self.mask_fc[2].bias.data.zero_()
+
+    def forward(self, data, rois):
+        assert data.size(1) == self.out_channels
+        if self.no_trans:
+            offset = data.new_empty(0)
+            return deform_roi_pooling(
+                data, rois, offset, self.spatial_scale, self.out_size,
+                self.out_channels, self.no_trans, self.group_size,
+                self.part_size, self.sample_per_part, self.trans_std)
+        else:
+            n = rois.shape[0]
+            offset = data.new_empty(0)
+            x = deform_roi_pooling(data, rois, offset, self.spatial_scale,
+                                   self.out_size, self.out_channels, True,
+                                   self.group_size, self.part_size,
+                                   self.sample_per_part, self.trans_std)
+            offset = self.offset_fc(x.view(n, -1))
+            offset = offset.view(n, 2, self.out_size, self.out_size)
+            mask = self.mask_fc(x.view(n, -1))
+            mask = mask.view(n, 1, self.out_size, self.out_size)
+            return deform_roi_pooling(
+                data, rois, offset, self.spatial_scale, self.out_size,
+                self.out_channels, self.no_trans, self.group_size,
+                self.part_size, self.sample_per_part, self.trans_std) * mask

maskrcnn_benchmark/layers/misc.py

@@ -11,6 +11,7 @@ is implemented
 
 import math
 import torch
+from torch import nn
 from torch.nn.modules.utils import _ntuple
 
 
@@ -108,3 +109,86 @@ def interpolate(
     output_shape = tuple(_output_size(2))
     output_shape = input.shape[:-2] + output_shape
     return _NewEmptyTensorOp.apply(input, output_shape)
+
+
+class DFConv2d(nn.Module):
+    """Deformable convolutional layer"""
+    def __init__(
+        self, 
+        in_channels, 
+        out_channels, 
+        with_modulated_dcn=True, 
+        kernel_size=3, 
+        stride=1, 
+        groups=1,
+        dilation=1,
+        deformable_groups=1,
+        bias=False
+    ):
+        super(DFConv2d, self).__init__()
+        if isinstance(kernel_size, (list, tuple)):
+            assert len(kernel_size) == 2
+            offset_base_channels = kernel_size[0] * kernel_size[1]
+        else:
+            offset_base_channels = kernel_size * kernel_size
+        if with_modulated_dcn:
+            from maskrcnn_benchmark.layers import ModulatedDeformConv
+            offset_channels = offset_base_channels * 3 #default: 27
+            conv_block = ModulatedDeformConv
+        else:
+            from maskrcnn_benchmark.layers import DeformConv
+            offset_channels = offset_base_channels * 2 #default: 18
+            conv_block = DeformConv
+        self.offset = Conv2d(
+            in_channels,
+            deformable_groups * offset_channels,
+            kernel_size=kernel_size,
+            stride= stride,
+            padding= dilation,
+            groups=1,
+            dilation=dilation
+        )           
+        for l in [self.offset,]:
+            nn.init.kaiming_uniform_(l.weight, a=1)
+            torch.nn.init.constant_(l.bias, 0.)
+        self.conv = conv_block(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride= stride,
+            padding=dilation,
+            dilation=dilation,
+            groups=groups,
+            deformable_groups=deformable_groups,
+            bias=bias
+        )
+        self.with_modulated_dcn = with_modulated_dcn
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = dilation
+        self.dilation = dilation
+
+    def forward(self, x):
+        if x.numel() > 0:
+            if not self.with_modulated_dcn:
+                offset = self.offset(x)
+                x = self.conv(x, offset)
+            else:
+                offset_mask = self.offset(x)
+                offset = offset_mask[:, :18, :, :]
+                mask = offset_mask[:, -9:, :, :].sigmoid()
+                x = self.conv(x, offset, mask)
+            return x
+        # get output shape
+        output_shape = [
+            (i + 2 * p - (di * (k - 1) + 1)) // d + 1
+            for i, p, di, k, d in zip(
+                x.shape[-2:], 
+                self.padding, 
+                self.dilation, 
+                self.kernel_size, 
+                self.stride
+            )
+        ]
+        output_shape = [x.shape[0], self.conv.weight.shape[0]] + output_shape
+        return _NewEmptyTensorOp.apply(x, output_shape)

maskrcnn_benchmark/modeling/backbone/resnet.py

@@ -24,6 +24,7 @@ from torch import nn
 
 from maskrcnn_benchmark.layers import FrozenBatchNorm2d
 from maskrcnn_benchmark.layers import Conv2d
+from maskrcnn_benchmark.layers import DFConv2d
 from maskrcnn_benchmark.modeling.make_layers import group_norm
 from maskrcnn_benchmark.utils.registry import Registry
 
@@ -106,6 +107,7 @@ class ResNet(nn.Module):
             stage2_relative_factor = 2 ** (stage_spec.index - 1)
             bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
             out_channels = stage2_out_channels * stage2_relative_factor
+            stage_with_dcn = cfg.MODEL.RESNETS.STAGE_WITH_DCN[stage_spec.index -1]
             module = _make_stage(
                 transformation_module,
                 in_channels,
@@ -115,6 +117,11 @@ class ResNet(nn.Module):
                 num_groups,
                 cfg.MODEL.RESNETS.STRIDE_IN_1X1,
                 first_stride=int(stage_spec.index > 1) + 1,
+                dcn_config={
+                    "stage_with_dcn": stage_with_dcn,
+                    "with_modulated_dcn": cfg.MODEL.RESNETS.WITH_MODULATED_DCN,
+                    "deformable_groups": cfg.MODEL.RESNETS.DEFORMABLE_GROUPS,
+                }
             )
             in_channels = out_channels
             self.add_module(name, module)
@@ -155,7 +162,8 @@ class ResNetHead(nn.Module):
         stride_in_1x1=True,
         stride_init=None,
         res2_out_channels=256,
-        dilation=1
+        dilation=1,
+        dcn_config={}
     ):
         super(ResNetHead, self).__init__()
 
@@ -182,7 +190,8 @@ class ResNetHead(nn.Module):
                 num_groups,
                 stride_in_1x1,
                 first_stride=stride,
-                dilation=dilation
+                dilation=dilation,
+                dcn_config=dcn_config
             )
             stride = None
             self.add_module(name, module)
@@ -204,7 +213,8 @@ def _make_stage(
     num_groups,
     stride_in_1x1,
     first_stride,
-    dilation=1
+    dilation=1,
+    dcn_config={}
 ):
     blocks = []
     stride = first_stride
@@ -217,7 +227,8 @@ def _make_stage(
                 num_groups,
                 stride_in_1x1,
                 stride,
-                dilation=dilation
+                dilation=dilation,
+                dcn_config=dcn_config
             )
         )
         stride = 1
@@ -235,7 +246,8 @@ class Bottleneck(nn.Module):
         stride_in_1x1,
         stride,
         dilation,
-        norm_func
+        norm_func,
+        dcn_config
     ):
         super(Bottleneck, self).__init__()
 
@@ -271,7 +283,22 @@ class Bottleneck(nn.Module):
         )
         self.bn1 = norm_func(bottleneck_channels)
         # TODO: specify init for the above
-
+        with_dcn = dcn_config.get("stage_with_dcn", False)
+        if with_dcn:
+            deformable_groups = dcn_config.get("deformable_groups", 1)
+            with_modulated_dcn = dcn_config.get("with_modulated_dcn", False)
+            self.conv2 = DFConv2d(
+                bottleneck_channels, 
+                bottleneck_channels, 
+                with_modulated_dcn=with_modulated_dcn, 
+                kernel_size=3, 
+                stride=stride_3x3, 
+                groups=num_groups,
+                dilation=dilation,
+                deformable_groups=deformable_groups,
+                bias=False
+            )
+        else:
             self.conv2 = Conv2d(
                 bottleneck_channels,
                 bottleneck_channels,
@@ -282,6 +309,8 @@ class Bottleneck(nn.Module):
                 groups=num_groups,
                 dilation=dilation
             )
+            nn.init.kaiming_uniform_(self.conv2.weight, a=1)
+
         self.bn2 = norm_func(bottleneck_channels)
 
         self.conv3 = Conv2d(
@@ -289,7 +318,7 @@ class Bottleneck(nn.Module):
         )
         self.bn3 = norm_func(out_channels)
 
-        for l in [self.conv1, self.conv2, self.conv3,]:
+        for l in [self.conv1, self.conv3,]:
             nn.init.kaiming_uniform_(l.weight, a=1)
 
     def forward(self, x):
@@ -346,7 +375,8 @@ class BottleneckWithFixedBatchNorm(Bottleneck):
         num_groups=1,
         stride_in_1x1=True,
         stride=1,
-        dilation=1
+        dilation=1,
+        dcn_config={}
     ):
         super(BottleneckWithFixedBatchNorm, self).__init__(
             in_channels=in_channels,
@@ -356,7 +386,8 @@ class BottleneckWithFixedBatchNorm(Bottleneck):
             stride_in_1x1=stride_in_1x1,
             stride=stride,
             dilation=dilation,
-            norm_func=FrozenBatchNorm2d
+            norm_func=FrozenBatchNorm2d,
+            dcn_config=dcn_config
         )
 
 
@@ -376,7 +407,8 @@ class BottleneckWithGN(Bottleneck):
         num_groups=1,
         stride_in_1x1=True,
         stride=1,
-        dilation=1
+        dilation=1,
+        dcn_config={}
     ):
         super(BottleneckWithGN, self).__init__(
             in_channels=in_channels,
@@ -386,7 +418,8 @@ class BottleneckWithGN(Bottleneck):
             stride_in_1x1=stride_in_1x1,
             stride=stride,
             dilation=dilation,
-            norm_func=group_norm
+            norm_func=group_norm,
+            dcn_config=dcn_config
         )
 
 

maskrcnn_benchmark/utils/c2_model_loading.py

@@ -143,6 +143,33 @@ def _load_c2_pickled_weights(file_path):
     return weights
 
 
+def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg):
+    import re
+    logger = logging.getLogger(__name__)
+    logger.info("Remapping conv weights for deformable conv weights")
+    layer_keys = sorted(state_dict.keys())
+    for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1):
+        if not stage_with_dcn:
+            continue
+        for old_key in layer_keys:
+            pattern = ".*layer{}.*conv2.*".format(ix)
+            r = re.match(pattern, old_key)
+            if r is None:
+                continue
+            for param in ["weight", "bias"]:
+                if old_key.find(param) is -1:
+                    continue
+                new_key = old_key.replace(
+                    "conv2.{}".format(param), "conv2.conv.{}".format(param)
+                )
+                logger.info("pattern: {}, old_key: {}, new_key: {}".format(
+                    pattern, old_key, new_key
+                ))
+                state_dict[new_key] = state_dict[old_key]
+                del state_dict[old_key]
+    return state_dict
+
+
 _C2_STAGE_NAMES = {
     "R-50": ["1.2", "2.3", "3.5", "4.2"],
     "R-101": ["1.2", "2.3", "3.22", "4.2"],
@@ -168,6 +195,10 @@ def load_resnet_c2_format(cfg, f):
     arch = arch.replace("-RETINANET", "")
     stages = _C2_STAGE_NAMES[arch]
     state_dict = _rename_weights_for_resnet(state_dict, stages)
+    # ***********************************
+    # for deformable convolutional layer
+    state_dict = _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg)
+    # ***********************************
     return dict(model=state_dict)